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Application of Evidence Synthesis: Effectiveness of immunosuppressive therapies in renal transplantation Jim Chilcott Mike Holmes Work undertaken as part of a submission to NICE on behalf of Novartis UK Immunosuppression in Renal Transplantation • One of the most serious problems facing renal transplant patients is possibility that their own bodies try to reject or destroy the transplant. • One of the best predictors of long term graft loss is the incidence of acute rejection - the most commonly defined outcome measure in renal transplant trials. • To avoid acute rejection patients regularly take a cocktail of immunosuppressive drugs usually for the rest of their lives. NICE are currently in the process of attempting to construct guidance in this field and a key element of this appraisal is the assessment of evidence on the impact of drug regimens on acute rejection. Comparators • Immunosuppressive regimens can consist of: An induction drug used at the time of transplantation 3 (4) Basiliximab / Daclizumab / None ( /ATG) Base maintenance drug 6 Sandimmun / 2x Neoral / Tacrolimus / 2x Sirolimus Adjunctive drug 3 (4) Azathiopine / 2x MMF / None Giving 54 (96) different regimens - excluding dosing strategies etc • For the purposes of this assessment this was reduced to a set of 16 different regimens, based upon common usage, licensing etc. Problems with the Evidence Base Direct evidence base consists of a set of RCTs with two / three comparators. Full complement of problems that may beset an experimental evidence base: • • • • Multiple pieces of evidence on individual comparisons. Different types of outcome. Different measures of effect. Biases to internal validity – For example, conflicting biases from under and over drug dosing between arms in the trials. • Biases to external validity – For example, consistent under or over drug dosing compared to common practice. • Indirect evidence – Whilst acute rejection is in itself of disbenefit to a patient, the primary motivation in preventing acute rejection is in reducing potential associated long term graft loss. Thus virtually the entire randomised evidence is indirect. • • Mixed comparisons – As discussed above. Gaps in the experimental evidence. The AR evidence base Sandimmune+ azathioprine Simulect+Neoral + azathioprine Sandimmune II I III Daclizumab+Neoral +azathioprine I Simulect+Neoral +MMF I I Neoral+ azathioprine Neoral+ MMF II I II Sandimmune+ MMF II Tacrolimus+ azathioprine I II Simulect+ Neoral I Tacrolimus+ MMF Neoral I I Daclizumab+ Neoral II Neoral C2 monitoring Sirolimus+Neoral (<3 months) Sirolimus+Neoral (continued) Synthesis of evidence on AR impact Two options for synthesising evidence • Bayesian synthesis of evidence Uses all the evidence in a coherent framework to estimate both relative and absolute treatment effects • Only alternative: series of standard pair-wise metaanalyses to estimate relative treatment effects bolted onto naïve average of baseline effects. Bayesian models of treatment effectiveness Two models of treatment effectiveness have been explored: • Treatment combination model Each regimen, defined by an induction, base maintenance and adjunct drug is considered as a separate therapy in estimating effectiveness. • Individual drug effect model Effectiveness of a regimen is assumed to be built up from the effectiveness of its constituent drugs. Has the potential advantage of making a fuller use of the available evidence, however assumes no interactions or synergistic effects between drugs. Both models assume a fixed treatment effect with a random baseline effect The treatment combination model Let there be n m trials, subscripted i, arms subscripted j, treatment combinations, subscripted k, Assume each trial baseline rejection rate, logit(mu(i)), (i.e. rejection rate under baseline therapy, in this case Sandimmun®(ciclosporin)) varies randomly and that mu(i) is normally distributed with mean m and standard deviation . i.e. mu(i) ~ N(m,2) Assume a fixed treatment effect, dk for treatment combination k. Let pi,j be the estimated probability of acute rejection in study i, arm j, under treatment k, then: logit(pi,j) = mu(i) + dk and pi,j = exp (mu(i) + dk) / (1+ exp (mu(i) + dk)) . Similarly where pk is the probability of acute rejection for an ‘average’ transplant population under treatment k, then: pk = exp (m + dk) / (1+ exp (m + dk)) . The individual drug effect model Assume that a combination maintenance treatment is defined as a baseline drug, together with an optional induction therapy and optional adjunct agent. Let there be n trials, subscripted i, m arms subscripted j, ind induction therapy drugs, subscripted kind, base baseline therapy drugs, subscripted kbase, adj adjunct therapy drugs, subscripted kadj, Assume each trial baseline rejection rate, logit(mu(i)), varies randomly and: mu(i) ~ N(m,2) Assume for each drug k a fixed treatment effect, dk . Let pi,j be the estimated probability of acute rejection in study i, arm j, under treatment kind+kbase+kadj, then: and . logit(pi,j) = mu(i) + dkind + dkbase + dkadj pi,j = exp (mu(i)+dkind +dkbase +dkadj) / (1+ exp (mu(i) +dkind +dkbase +dkadj)) Similarly for an ‘average’ transplant population under treatment k defined by kind, kbase and kadj then: pk = exp (m +dkind +dkbase +dkadj) / (1+ exp(m +dkind +dkbase +dkadj)) The Markov Chain Monte Carlo (MCMC) Technique • WinBUGS (Vs1.3) has been used to estimate posterior distributions for baseline and treatment effect parameters from vague priors. • Convergence has been assessed using Gelman-Rubin statistic. • First 5 thousand ‘burn in ‘ simulation discarded. Adequate convergence achieved within 20,000 iterations, i.e. G-R statistic converged to 1.00 . Prop of AR steroid Treatment Study n r responsive Actual Bugs Lower CI Upper CI Deviance Rank Sandimmune+Aza Niese (12 months) 41 23 56.1% 46.0% 39% 54% 1.94 Neoral+Aza Niese (12 months) 45 19 42.2% 38.8% 32% 47% 0.51 Sandimmune+Aza Abendroth, Buchholz (24 months) 27 15 55.6% 44.3% 37% 53% 1.56 Neoral+Aza Abendroth, Buchholz (24 months) 28 10 35.7% 37.1% 31% 45% 0.19 Sandimmune+Aza Pollard (12 months) 98 46 87.8 46.9% 43.0% 37% 49% 0.95 Neoral+Aza Pollard (12 months) 195 67 89.7 34.4% 35.9% 31% 41% 0.72 Sandimmune+Aza Miller (12 months) 207 96 74.9 46.4% 46.6% 41% 52% 0.63 Tacrolimus + Aza Miller (12 months) 205 63 89.3 30.7% 26.7% 23% 31% 2.22 Sandimmune+Aza Mayer () 145 63 43.4% 43.1% 38% 48% 0.43 Tacrolimus + Aza Mayer () 303 73 24.1% 24.0% 20% 28% 0.57 Neoral+Aza Morris-Stiff (6 months) 89 35 39.3% 38.7% 33% 46% 0.42 Tacrolimus + Aza Morris-Stiff (6 months) 90 29 32.2% 26.2% 21% 32% 1.98 Neoral+Aza Margreiter (6 months) 271 101 0.46 37.3% 34.6% 30% 39% 1.44 Tacrolimus + Aza Margreiter (6 months) 286 56 0.46 19.6% 23.0% 19% 27% 2.42 Tacrolimus + Aza Johnson (2000) (12 months) 76 13 0.31 17.1% 22.7% 17% 28% 1.71 Neoral + MMF (2mg/day) Johnson (2000) (12 months) 75 15 0.47 20.0% 20.3% 15% 27% 0.46 Tacrolimus + MMF (2mg/day) Johnson (2000) (12 months) 72 11 0.73 15.3% 12.5% 7% 20% 1.43 Neoral + MMF (2mg/day) Wang (12 month mean) 32 5 0.2 15.6% 19.8% 13% 27% 0.59 Tacrolimus + MMF (2mg/day) Wang (12 month mean) 25 1 0 4.0% 12.1% 6% 20% 2.17 Neoral + MMF (2mg/day) Kreis (2 years) 38 7 94.7 18.4% 20.9% 14% 29% 0.42 Neoral McDonald (6 months) 130 54 91.5 41.5% 45.3% 39% 51% 1.24 Sirolimus (2mg/day) + Neoral McDonald (6 months) 227 56 96 24.7% 20.7% 17% 25% 2.93 Sirolimus (N+S<3mnths) Johnson (2001) (12 months) 215 43 20.0% 19.2% 15% 24% 0.87 Sirolimus (2mg/day) + Neoral Johnson (2001) (12 months) 215 29 13.5% 17.1% 13% 21% 2.69 Sirolimus (N+S<3mnths) Gonwa (6 months) 92 17 18.5% 20.3% 15% 26% 0.63 Sirolimus (2mg/day) + Neoral Gonwa (6 months) 89 15 16.9% 18.1% 14% 23% 0.43 Sandimmune European MMF Cooperative Study Group (6 months) 166 77 46.4% 46.9% 39% 54% 0.97 Sandimmune + MMF (2mg) European MMF Cooperative Study Group (6 months) 165 28 17.0% 18.8% 15% 23% 0.90 Sandimmune+Aza Sollinger (6 months) 164 63 38.4% 41.0% 35% 46% 1.00 Sandimmune + MMF (2mg) Sollinger (6 months) 165 33 20.0% 19.3% 15% 24% 0.57 Sandimmune+Aza Tricontinental MMF Renal Transplantation Study 164Group (6 59 months) 36.0% 40.1% 34% 46% 1.73 Sandimmune + MMF (2mg) Tricontinental MMF Renal Transplantation Study 171Group (6 34 months) 19.9% 18.7% 15% 23% 0.68 Neoral Nashan (1997) (6 months) 186 73 0.41 39.2% 40.7% 35% 46% 0.79 Basiliximab+Neoral Nashan (1997) (6 months) 190 51 0.63 26.8% 28.3% 23% 34% 0.90 Neoral+Aza Ponticelli (6 months) 172 50 0.66 29.1% 32.5% 27% 38% 1.56 Basiliximab+Neoral+aza Ponticelli (6 months) 168 31 0.71 18.5% 18.5% 13% 25% 1.03 Neoral Kahan (12 months) 173 85 0.40 49.1% 46.8% 41% 53% 0.97 Basiliximab+Neoral Kahan (12 months) 173 61 0.44 35.3% 33.6% 28% 40% 0.95 Neoral Charpentier (6 months) 134 64 0.67 47.8% 45.2% 39% 52% 0.90 Daclizumab+Neoral Charpentier (6 months) 141 39 0.72 27.7% 27.6% 21% 35% 0.99 Neoral+Aza Vincenti (12 months) 134 47 0.60 35.1% 35.3% 30% 41% 0.50 Daclizumab+Neoral+aza Vincenti (12 months) 126 28 0.64 22.2% 22.2% 16% 30% 0.99 Neoral + MMF (2mg/day) Lawen (6 months) 64 17 0.71 26.6% 22.8% 16% 31% 1.03 Basiliximab+Neoral+MMF Lawen (6 months) 59 9 0.67 15.3% 13.2% 7% 21% 1.01 Basiliximab+Neoral+MMF Lebranchu (6 months) 36 3 8.3% 11.9% 6% 20% 0.79 Neoral C2 monitoring Mo2art (3 months) 117 12 10.3% 10.2% 5% 16% 1.00 7 39 11 46 24 32 34 4 43 38 45 6 12 3 9 41 13 36 5 44 14 1 29 2 35 42 22 26 18 37 8 33 30 28 10 16 23 25 26 21 40 20 15 17 31 19 LeadAuthor Niese Niese Abendroth, Buchholz Abendroth, Buchholz Pollard Pollard Miller Miller Mayer Mayer Morris-Stiff Morris-Stiff Strategy Sandimmun+Aza Neoral+Aza Sandimmun+Aza Neoral+Aza Sandimmun+Aza Neoral+Aza Sandimmun+Aza Tacrolimus + Aza Sandimmun+Aza Tacrolimus + Aza Neoral+Aza Tacrolimus + Aza n 41 45 27 28 98 195 207 205 145 303 89 90 r Actual 23 56.1% 19 42.2% 15 55.6% 10 35.7% 46 46.9% 67 34.4% 96 46.4% 63 30.7% 63 43.4% 73 24.1% 35 39.3% 29 32.2% Bugs estimate Lower CI Upper CI Deviance Rank 45.6% 39% 54% 2.06 5 38.2% 32% 46% 0.56 39 44.1% 37% 52% 1.58 9 36.8% 31% 44% 0.16 46 43.1% 38% 49% 0.91 29 35.8% 31% 41% 0.66 34 46.2% 41% 52% 0.61 37 26.5% 22% 31% 2.41 2 43.1% 38% 48% 0.40 44 24.1% 21% 28% 0.55 40 38.2% 33% 45% 0.43 43 26.0% 22% 32% 2.08 4 Model consistency checking Using the combination treatment effect model the individual trial arms were ranked in descending order of deviance contribution. All trials with an arm with a deviance contribution greater than 1.8 were examined to identify possible causes of major bias. Amendments and exclusions were applied as a result of this consistency checking. • The Barone study comparing Sandimmun and Neoral®(ciclosporin microemulsion).6 DC of 3.43 in the Sandimmun arm. Statistically significant difference in the baseline serum creatinine levels, Sandimmun 3.4 mg/dl vs Neoral 4.4 mg/dl (p<0.05). The serum creatinine levels in the Sandimmun arm equate to approximately 300 mmols/l, in the other Neoral/Sandimmun studies identified in this review levels baseline serum creatinine levels were all in the order of 600 – 700 mmols/l,1,27 and no other studies reported statistically significant baseline levels, thus this study was excluded. Model consistency checking II • The Trompeter study32 had a DC of 2.21 in the Neoral arm. This study was excluded since it is the only paediatric study within the review with implications for both the underlying population and treatment protocols. • Initially the interim results for Morris-Stiff 26 were included in the assessment, however this study gave rise to deviance contributions of 3.95 in the Tacrolimus arm and 2.07 in the Neoral arm. These results however were for 3 months follow up and only included the first 80 patients. Subsequent results have been reported for 179 patients with 6 months follow up, 25 though these results are only recorded in a letter their inclusion is justified on the basis of the previously reported and reviewed study design. These 6 month results have been used in the final assessment (DCs Tac 2.08, Neoral 0.43) • In the initial analysis the long term Sirolimus and Neoral combination arms of McDonald22 and Johnson 21 had deviance contributions of 1.91 and 1.86 respectively. On closer examination the Johnson study randomises at 3 months post transplant and reports post 3 acute rejection rates. Thus for this assessment the Johnson figures have been adjusted by adding the initial 3 month acute rejection rate onto both arms. This adjustment leads to deviance contributions of 1.53 and 0.87 compared to the above. Model consistency checking III Of the five tacrolimus plus azathioprine studies, four studies have deviances greater than 1.8, in fact this subset of studies include the arms ranked 1,2,4 and 7 in terms of their deviance contributions, this indicates that there is a great deal of inconsistency within tacrolimus evidence base, this concurs with the results of the systematic review of tacrolimus. An area for further research might to explore the impact of internal and external biases within these trials including potentially the date of the trials, impact of blinding and the impact of Neoral and tacrolimus doses within the trials. Pre-synthesis systematic review identified: •Open label studies •Exclusion of high risk patients unclear whether before or after randomisation (ie 2 weeks after Tx) •Differential dosing between arms – 1 arm lower than licensing recommendations Model Fit Comparison Model Data points Parameters Posterior mean deviance Deviance Information Critirion Treatment Drug model model 46 46 16 10 49.5 60.48 273.6 284.6 Sa San nd di im m m m un une e+ Az a Sa nd T Ne Ne im ac or ora r Ne mu olim al+ l Ta o ne u Az cr ral + s+ a ol im + M MM Az u a M F Si s + F ( (2m ro M 2 lim M mg g) us F ( /da (N 2m y) B Ba as +S< g/da il y s 3 Ba ilix ixim mn ) sil ima a b ths ix im b+N +N e ) ab e or or a + l N a Da Da c eo l+a Si ro cliz lizu r al+ za lim u m M us ma a b MF (2 b+N +N m eo eor g Ne /d r a al or ay) l+a al + z C2 N a e m or on al ito r in g Synthesis of evidence on acute rejection rates by treatment 70% 60% 50% 40% 30% 20% 10% 0% av - AR av - LCI av - UCI AR Rates Treatment av - AR av - LCI av - UCI Sandimmune 49% 39% 59% Sandimmune+Aza 43% 38% 47% Neoral 43% 37% 49% Neoral+Aza 36% 32% 40% Tacrolimus + Aza 24% 20% 27% Sandimmune + MMF (2mg) 20% 16% 25% Neoral + MMF (2mg/day) 21% 16% 28% Tacrolimus + MMF (2mg/day) 13% 7% 21% Sirolimus (N+S<3mnths) 22% 16% 29% Basiliximab+Neoral 30% 24% 37% Basiliximab+Neoral+aza 21% 14% 29% Basiliximab+Neoral+MMF 12% 6% 20% Daclizumab+Neoral 26% 18% 35% Daclizumab+Neoral+aza 22% 15% 31% Sirolimus (2mg/day) + Neoral 19% 15% 24% Neoral C2 monitoring 10% 5% 18% Limitations and provisos A number of possible alternatives in the model formulation exist: • Use of a random effects model rather than fixed effects • Remains a high degree of inconsistency within the model – potential to explore other factors for example: • • Date of trial • Blinded vs Open label RCTs • Baseline serum creatinine levels • Drug doses • Patient characteristics – eg HLA matching, previous transplants. Combination treatment effect model evidence base contains 2 unlinked networks, comparisons between networks have low validity. Bayesian synthesis vs Pairwise meta-analysis AR rates within 1 year Basic Triple Therapy CAS Comparators TAS CAS + BAS CAS + DAC CMS Pairs Pairs Pairs Pairs 38.9 34 32.8 40.8 20 20.2 25.2 19.4 Novartis/ Pairs Winbugs 34 Mean (excl. Winbugs) 36 24 20 22 21 Clinical and cost effectiveness of immunosuppressive regimens in renal transplantation. West Midlands Health Technology Assessment Group, Department of Public Health and Epidemiology, The University of Birmingham. Dec 2002. Critique of Bayesian synthesis I The first criticism is that the “requirement to consider individually the relevance and/or quality of each study for the specific question under consideration, and hence attach a degree of uncertainty to the ‘evidence’ from each study” is not fulfilled.” # True but equally applicable to standard meta-analysis Bayesian synthesis highlights some inconsistencies and facilitates incorporation of explanatory factors and quantified bias adjustment. # Clinical and cost effectiveness of immunosuppressive regimens in renal transplantation. West Midlands Health Technology Assessment Group, Department of Public Health and Epidemiology, The University of Birmingham. Dec 2002. Critique of Bayesian synthesis II “Synthesis falls into the ‘trap’ of overdependence on absolute arm results… Rejection rates for an ‘average’ population given a treatment are very close to the average of the observed rates over the trials – which suggests that (all the mi are similar in size) it is the ‘treatment effects’ that are having to model all variation due both to treatment and to inter-trial variation in risk.” # Impact of consistent and differential bias between and especially within studies presents an equal problem to standard meta-analysis. Differential bias in the treatment arms presents itself in the Bayesian synthesis as inconsistency which in this particular example ties in with poor quality ratings for specific trials. Conclusions for Renal Transplantation • Combination treatment model provides best fit to data. Indicates possibly important interaction between the drugs when used in combinations, supports clinical or pharmacological opinion. • Though baseline results provide rank ordering of effectiveness of combination therapies, CIs for many regimens have a large overlap indicating a high degree of uncertainty. • Basiliximab and daclizumab have very similar effectiveness profiles across the different drug combinations that have been investigated. • Subset of the evidence with the worst consistency is the evidence for tacrolimus. The results for tacrolimus should therefore be viewed with particular caution. Key Challenges • Further research is required comparing standard meta-analyses with Bayesian syntheses in complex decision problems. • Further development is required in protocols for model consistency checking. Including diagnostic measures of consistency and guidelines for implementation. • Methods for incorporating quantified estimates of bias: eg impact of open label studies on estimates of effectiveness. • Methods for incorporating prognostic factors and non random effects.